import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
df= pd.read_csv("Df_Final.csv")
df["FIPS"]=df["FIPS"].apply(str).str.zfill(5)
#df
| FIPS | PovertyRate | MedianFamilyIncome | TractBlack | TractHispanic | lasnap1 | lasnap10 | lahunv1 | lahunv10 | TractPop | ... | Avg w Mortage | Avg w/o Mortage | Regions | State | Subregions | Income_Group | LogIncome | LogPop | LogMortgage | Insecurity_Groups | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01001 | 0.128072 | 64137.95386 | 0.176706 | 0.024005 | 0.033398 | 0.008940 | 0.012418 | 0.003070 | 54571 | ... | 141300 | 354 | South | Alabama | East South Central Division | Middle | 11.068792 | 10.907258 | 11.85864057 | Above Average |
| 1 | 01003 | 0.138195 | 62404.36429 | 0.093847 | 0.043848 | 0.026746 | 0.000560 | 0.007649 | 0.000237 | 182265 | ... | 169300 | 365 | South | Alabama | East South Central Division | Middle | 11.041390 | 12.113217 | 12.03942757 | Above Average |
| 2 | 01005 | 0.240037 | 43916.97214 | 0.468915 | 0.050515 | 0.049445 | 0.013646 | 0.015484 | 0.005828 | 27457 | ... | 92200 | 334 | South | Alabama | East South Central Division | Middle | 10.690056 | 10.220376 | 11.43171541 | Extreme |
| 3 | 01007 | 0.170591 | 42271.68510 | 0.220249 | 0.017718 | 0.042426 | 0.000711 | 0.009792 | 0.000037 | 22915 | ... | 102700 | 344 | South | Alabama | East South Central Division | Middle | 10.651873 | 10.039547 | 11.5395674 | High |
| 4 | 01009 | 0.174327 | 53069.87190 | 0.013276 | 0.080702 | 0.046853 | 0.001742 | 0.012559 | 0.000333 | 57322 | ... | 119800 | 335 | South | Alabama | East South Central Division | Middle | 10.879365 | 10.956440 | 11.69357896 | Average |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3135 | 56037 | 0.123416 | 80227.47560 | 0.009999 | 0.152696 | 0.010114 | 0.000977 | 0.008209 | 0.000541 | 43806 | ... | 190900 | 361 | West | Wyoming | Mountain Division | Middle | 11.292621 | 10.687526 | 12.15950501 | Average |
| 3136 | 56039 | 0.082756 | 89150.63642 | 0.002301 | 0.149854 | 0.004115 | 0.000000 | 0.002276 | 0.000000 | 21294 | ... | 689000 | 675 | West | Wyoming | Mountain Division | Middle | 11.398083 | 9.966181 | 13.44299655 | Average |
| 3137 | 56041 | 0.140232 | 65319.39516 | 0.002604 | 0.087840 | 0.016383 | 0.000965 | 0.010021 | 0.000524 | 21118 | ... | 176700 | 342 | West | Wyoming | Mountain Division | Middle | 11.087044 | 9.957881 | 12.08220866 | Above Average |
| 3138 | 56043 | 0.142366 | 62597.28724 | 0.002578 | 0.136177 | 0.011748 | 0.004197 | 0.004926 | 0.001535 | 8533 | ... | 160800 | 365 | West | Wyoming | Mountain Division | Middle | 11.044477 | 9.051696 | 11.98791664 | Average |
| 3139 | 56045 | 0.123334 | 74728.71865 | 0.002913 | 0.029967 | 0.012483 | 0.001934 | 0.015628 | 0.005230 | 7208 | ... | 178200 | 380 | West | Wyoming | Mountain Division | Middle | 11.221620 | 8.882947 | 12.09066179 | Above Average |
3140 rows × 22 columns
from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)
import plotly.express as px
fig = px.choropleth_mapbox(df, geojson=counties, locations='FIPS', color='Food Insecurity Rate',
color_continuous_scale="jet",
range_color=(0,0.4),
mapbox_style="carto-positron",
zoom=3, center = {"lat": 37.0902, "lon": -95.7129},
opacity=0.9,
labels={'Food Insecurity Rate':'Food Insecurity Rate'}
)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
print(df['MedianFamilyIncome'].nlargest(1))
print(df['MedianFamilyIncome'].nsmallest(1))
2923 157191.8651 Name: MedianFamilyIncome, dtype: float64 1425 26621.94145 Name: MedianFamilyIncome, dtype: float64
fig = px.histogram(df, x="Food Insecurity Rate", color="Regions")
fig.show()
fig = px.histogram(df, x="Food Insecurity Rate", color="Subregions")
fig.show()
fig = px.histogram(df, x='MedianFamilyIncome', color='Insecurity_Groups')
fig.show()
fig = px.histogram(df, x="Food Insecurity Rate", color="Income_Group")
fig.show()
fig = px.box(df, x="Subregions", y="Food Insecurity Rate", points="all", color= "Income_Group")
fig.show()
fig = px.box(df, x="Subregions", y="Food Insecurity Rate", points="all", color= "Subregions")
fig.show()
fig = px.histogram(df, x="Food Insecurity Rate", color="Regions")
fig.show()
fig = px.histogram(df, x="Food Insecurity Rate", color="Income_Group")
fig.show()
Score_Card=df.groupby('Income_Group').describe()
fig = px.box(df, x="Regions", y="Food Insecurity Rate", points="all", color= "Income_Group")
fig.show()
fig = px.box(df, x="Regions", y="Food Insecurity Rate", points="all", color= "Regions")
fig.show()
fig = px.scatter(df, x="Food Insecurity Rate", y="MedianFamilyIncome", color="Subregions")
fig.show()
fig = px.scatter(df, x="Food Insecurity Rate", y="MedianFamilyIncome", color="Regions")
fig.show()
#Score_Card.to_csv("Df_Score_Card")
Score_Card
| PovertyRate | MedianFamilyIncome | ... | LogIncome | LogPop | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | mean | std | min | 25% | 50% | 75% | max | count | mean | ... | 75% | max | count | mean | std | min | 25% | 50% | 75% | max | |
| Income_Group | |||||||||||||||||||||
| High | 10.0 | 0.054391 | 0.014605 | 0.038254 | 0.045789 | 0.051003 | 0.058653 | 0.087542 | 10.0 | 131664.919950 | ... | 11.809676 | 11.965222 | 10.0 | 12.191512 | 1.508865 | 9.419953 | 11.882756 | 12.609644 | 13.001791 | 13.894068 |
| Low | 190.0 | 0.297190 | 0.065482 | 0.047208 | 0.253807 | 0.294577 | 0.339830 | 0.478627 | 190.0 | 36370.134246 | ... | 10.575431 | 10.598553 | 190.0 | 9.525205 | 0.885037 | 6.889591 | 9.093554 | 9.503999 | 10.021535 | 13.560320 |
| Middle | 2940.0 | 0.161046 | 0.055880 | 0.010000 | 0.119639 | 0.157625 | 0.195493 | 0.444440 | 2940.0 | 58932.670830 | ... | 11.077911 | 11.695048 | 2940.0 | 10.308911 | 1.478281 | 4.406719 | 9.355133 | 10.223376 | 11.171990 | 16.099790 |
3 rows × 112 columns